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T-Coffee Documentation Release Version 13.45.47.Aba98c5
T-Coffee Documentation Release Version_13.45.47.aba98c5 Cedric Notredame Aug 31, 2021 Contents 1 T-Coffee Installation 3 1.1 Installation................................................3 1.1.1 Unix/Linux Binaries......................................4 1.1.2 MacOS Binaries - Updated...................................4 1.1.3 Installation From Source/Binaries downloader (Mac OSX/Linux)...............4 1.2 Template based modes: PSI/TM-Coffee and Expresso.........................5 1.2.1 Why do I need BLAST with T-Coffee?.............................6 1.2.2 Using a BLAST local version on Unix.............................6 1.2.3 Using the EBI BLAST client..................................6 1.2.4 Using the NCBI BLAST client.................................7 1.2.5 Using another client.......................................7 1.3 Troubleshooting.............................................7 1.3.1 Third party packages......................................7 1.3.2 M-Coffee parameters......................................9 1.3.3 Structural modes (using PDB)................................. 10 1.3.4 R-Coffee associated packages................................. 10 2 Quick Start Regressive Algorithm 11 2.1 Introduction............................................... 11 2.2 Installation from source......................................... 12 2.3 Examples................................................. 12 2.3.1 Fast and accurate........................................ 12 2.3.2 Slower and more accurate.................................... 12 2.3.3 Very Fast........................................... -
How to Generate a Publication-Quality Multiple Sequence Alignment (Thomas Weimbs, University of California Santa Barbara, 11/2012)
Tutorial: How to generate a publication-quality multiple sequence alignment (Thomas Weimbs, University of California Santa Barbara, 11/2012) 1) Get your sequences in FASTA format: • Go to the NCBI website; find your sequences and display them in FASTA format. Each sequence should look like this (http://www.ncbi.nlm.nih.gov/protein/6678177?report=fasta): >gi|6678177|ref|NP_033320.1| syntaxin-4 [Mus musculus] MRDRTHELRQGDNISDDEDEVRVALVVHSGAARLGSPDDEFFQKVQTIRQTMAKLESKVRELEKQQVTIL ATPLPEESMKQGLQNLREEIKQLGREVRAQLKAIEPQKEEADENYNSVNTRMKKTQHGVLSQQFVELINK CNSMQSEYREKNVERIRRQLKITNAGMVSDEELEQMLDSGQSEVFVSNILKDTQVTRQALNEISARHSEI QQLERSIRELHEIFTFLATEVEMQGEMINRIEKNILSSADYVERGQEHVKIALENQKKARKKKVMIAICV SVTVLILAVIIGITITVG 2) In a text editor, paste all your sequences together (in the order that you would like them to appear in the end). It should look like this: >gi|6678177|ref|NP_033320.1| syntaxin-4 [Mus musculus] MRDRTHELRQGDNISDDEDEVRVALVVHSGAARLGSPDDEFFQKVQTIRQTMAKLESKVRELEKQQVTIL ATPLPEESMKQGLQNLREEIKQLGREVRAQLKAIEPQKEEADENYNSVNTRMKKTQHGVLSQQFVELINK CNSMQSEYREKNVERIRRQLKITNAGMVSDEELEQMLDSGQSEVFVSNILKDTQVTRQALNEISARHSEI QQLERSIRELHEIFTFLATEVEMQGEMINRIEKNILSSADYVERGQEHVKIALENQKKARKKKVMIAICV SVTVLILAVIIGITITVG >gi|151554658|gb|AAI47965.1| STX3 protein [Bos taurus] MKDRLEQLKAKQLTQDDDTDEVEIAVDNTAFMDEFFSEIEETRVNIDKISEHVEEAKRLYSVILSAPIPE PKTKDDLEQLTTEIKKRANNVRNKLKSMERHIEEDEVQSSADLRIRKSQHSVLSRKFVEVMTKYNEAQVD FRERSKGRIQRQLEITGKKTTDEELEEMLESGNPAIFTSGIIDSQISKQALSEIEGRHKDIVRLESSIKE LHDMFMDIAMLVENQGEMLDNIELNVMHTVDHVEKAREETKRAVKYQGQARKKLVIIIVIVVVLLGILAL IIGLSVGLK -
Treebase: an R Package for Discovery, Access and Manipulation of Online Phylogenies
Methods in Ecology and Evolution 2012, 3, 1060–1066 doi: 10.1111/j.2041-210X.2012.00247.x APPLICATION Treebase: an R package for discovery, access and manipulation of online phylogenies Carl Boettiger1* and Duncan Temple Lang2 1Center for Population Biology, University of California, Davis, CA,95616,USA; and 2Department of Statistics, University of California, Davis, CA,95616,USA Summary 1. The TreeBASE portal is an important and rapidly growing repository of phylogenetic data. The R statistical environment has also become a primary tool for applied phylogenetic analyses across a range of questions, from comparative evolution to community ecology to conservation planning. 2. We have developed treebase, an open-source software package (freely available from http://cran.r-project. org/web/packages/treebase) for the R programming environment, providing simplified, programmatic and inter- active access to phylogenetic data in the TreeBASE repository. 3. We illustrate how this package creates a bridge between the TreeBASE repository and the rapidly growing collection of R packages for phylogenetics that can reduce barriers to discovery and integration across phyloge- netic research. 4. We show how the treebase package can be used to facilitate replication of previous studies and testing of methods and hypotheses across a large sample of phylogenies, which may help make such important reproduc- ibility practices more common. Key-words: application programming interface, database, programmatic, R, software, TreeBASE, workflow include, but are not limited to, ancestral state reconstruction Introduction (Paradis 2004; Butler & King 2004), diversification analysis Applications that use phylogenetic information as part of their (Paradis 2004; Rabosky 2006; Harmon et al. -
NASP: an Accurate, Rapid Method for the Identification of Snps in WGS Datasets That Supports Flexible Input and Output Formats Jason Sahl
Himmelfarb Health Sciences Library, The George Washington University Health Sciences Research Commons Environmental and Occupational Health Faculty Environmental and Occupational Health Publications 8-2016 NASP: an accurate, rapid method for the identification of SNPs in WGS datasets that supports flexible input and output formats Jason Sahl Darrin Lemmer Jason Travis James Schupp John Gillece See next page for additional authors Follow this and additional works at: http://hsrc.himmelfarb.gwu.edu/sphhs_enviro_facpubs Part of the Bioinformatics Commons, Integrative Biology Commons, Research Methods in Life Sciences Commons, and the Systems Biology Commons APA Citation Sahl, J., Lemmer, D., Travis, J., Schupp, J., Gillece, J., Aziz, M., & +several additional authors (2016). NASP: an accurate, rapid method for the identification of SNPs in WGS datasets that supports flexible input and output formats. Microbial Genomics, 2 (8). http://dx.doi.org/10.1099/mgen.0.000074 This Journal Article is brought to you for free and open access by the Environmental and Occupational Health at Health Sciences Research Commons. It has been accepted for inclusion in Environmental and Occupational Health Faculty Publications by an authorized administrator of Health Sciences Research Commons. For more information, please contact [email protected]. Authors Jason Sahl, Darrin Lemmer, Jason Travis, James Schupp, John Gillece, Maliha Aziz, and +several additional authors This journal article is available at Health Sciences Research Commons: http://hsrc.himmelfarb.gwu.edu/sphhs_enviro_facpubs/212 Methods Paper NASP: an accurate, rapid method for the identification of SNPs in WGS datasets that supports flexible input and output formats Jason W. Sahl,1,2† Darrin Lemmer,1† Jason Travis,1 James M. -
Introduction to Linux for Bioinformatics – Part II Paul Stothard, 2006-09-20
Introduction to Linux for bioinformatics – part II Paul Stothard, 2006-09-20 In the previous guide you learned how to log in to a Linux account, and you were introduced to some basic Linux commands. This section covers some more advanced commands and features of the Linux operating system. It also introduces some command-line bioinformatics programs. One important aspect of using a Linux system from a Windows or Mac environment that was not discussed in the previous section is how to transfer files between computers. For example, you may have a collection of sequence records on your Windows desktop that you wish to analyze using a Linux command-line program. Alternatively, you may want to transfer some sequence analysis results from a Linux system to your Mac so that you can add them to a PowerPoint presentation. Transferring files between Mac OS X and Linux Recall that Mac OS X includes a Terminal application (located in the Applications >> Utilities folder), which can be used to log in to other systems. This terminal can also be used to transfer files, thanks to the scp command. Try transferring a file from your Mac to your Linux account using the Terminal application: 1. Launch the Terminal program. 2. Instead of logging in to your Linux account, use the same basic commands you learned in the previous section (pwd, ls, and cd) to navigate your Mac file system. 3. Switch to your home directory on the Mac using the command cd ~ 4. Create a text file containing your home directory listing using ls -l > myfiles.txt 5. -
Introduction to Bioinformatics (Elective) – SBB1609
SCHOOL OF BIO AND CHEMICAL ENGINEERING DEPARTMENT OF BIOTECHNOLOGY Unit 1 – Introduction to Bioinformatics (Elective) – SBB1609 1 I HISTORY OF BIOINFORMATICS Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biologicaldata. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to analyze and interpret biological data. Bioinformatics has been used for in silico analyses of biological queries using mathematical and statistical techniques. Bioinformatics derives knowledge from computer analysis of biological data. These can consist of the information stored in the genetic code, but also experimental results from various sources, patient statistics, and scientific literature. Research in bioinformatics includes method development for storage, retrieval, and analysis of the data. Bioinformatics is a rapidly developing branch of biology and is highly interdisciplinary, using techniques and concepts from informatics, statistics, mathematics, chemistry, biochemistry, physics, and linguistics. It has many practical applications in different areas of biology and medicine. Bioinformatics: Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data. Computational Biology: The development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems. "Classical" bioinformatics: "The mathematical, statistical and computing methods that aim to solve biological problems using DNA and amino acid sequences and related information.” The National Center for Biotechnology Information (NCBI 2001) defines bioinformatics as: "Bioinformatics is the field of science in which biology, computer science, and information technology merge into a single discipline. -
A Tool to Sanity Check and If Needed Reformat FASTA Files
bioRxiv preprint doi: https://doi.org/10.1101/024448; this version posted August 13, 2015. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Fasta-O-Matic: a tool to sanity check and if needed reformat FASTA files Jennifer Shelton Kansas State University August 11, 2015 Abstract As the shear volume of bioinformatic sequence data increases the only way to take advantage of this content is to more completely automate ro- bust analysis workflows. Analysis bottlenecks are often mundane and overlooked processing steps. Idiosyncrasies in reading and/or writing bioinformatics file formats can halt or impair analysis workflows by in- terfering with the transfer of data from one informatics tools to another. Fasta-O-Matic automates handling of common but minor format issues that otherwise may halt pipelines. The need for automation must be balanced by the need for manual confirmation that any formatting error is actually minor rather than indicative of a corrupt data file. To that end Fasta-O-Matic reports any issues detected to the user with optionally color coded and quiet or verbose logs. Fasta-O-Matic can be used as a general pre-processing tool in bioin- formatics workflows (e.g. to automatically wrap FASTA files so that they can be read by BioPerl). It was also developed as a sanity check for bioinformatic core facilities that tend to repeat common analysis steps on FASTA files received from disparate sources. -
A Tool to Sanity Check and If Needed Reformat FASTA Files
ManuscriptbioRxiv preprint doi: https://doi.org/10.1101/024448; this version posted August 21, 2015. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under Click here to download Manuscript: bmc_article.tex aCC-BY 4.0 International license. Click here to view linked References Shelton and Brown 1 2 3 4 5 RESEARCH 6 7 8 9 Fasta-O-Matic: a tool to sanity check and if 10 11 needed reformat FASTA files 12 13 Jennifer M Shelton1 and Susan J Brown1* 14 15 *Correspondence: [email protected] 16 1KSU/K-INBRE Bioinformatics Abstract 17 Center, Division of Biology, 18 Kansas State University, Background: As the sheer volume of bioinformatic sequence data increases, the 19 Manhattan, KS, USA only way to take advantage of this content is to more completely automate Full list of author information is 20 available at the end of the article robust analysis workflows. Analysis bottlenecks are often mundane and 21 overlooked processing steps. Idiosyncrasies in reading and/or writing 22 bioinformatics file formats can halt or impair analysis workflows by interfering 23 with the transfer of data from one informatics tools to another. 24 Results: Fasta-O-Matic automates handling of common but minor formatissues 25 that otherwise may halt pipelines. The need for automation must be balanced by 26 the need for manual confirmation that any formatting error is actually minor 27 28 rather than indicative of a corrupt data file. -
The FASTA Program Package Introduction
fasta-36.3.8 December 4, 2020 The FASTA program package Introduction This documentation describes the version 36 of the FASTA program package (see W. R. Pearson and D. J. Lipman (1988), “Improved Tools for Biological Sequence Analysis”, PNAS 85:2444-2448, [17] W. R. Pearson (1996) “Effective protein sequence comparison” Meth. Enzymol. 266:227- 258 [15]; and Pearson et. al. (1997) Genomics 46:24-36 [18]. Version 3 of the FASTA packages contains many programs for searching DNA and protein databases and for evaluating statistical significance from randomly shuffled sequences. This document is divided into four sections: (1) A summary overview of the programs in the FASTA3 package; (2) A guide to using the FASTA programs; (3) A guide to installing the programs and databases. Section (4) provides answers to some Frequently Asked Questions (FAQs). In addition to this document, the changes v36.html, changes v35.html and changes v34.html files list functional changes to the programs. The readme.v30..v36 files provide a more complete revision history of the programs, including bug fixes. The programs are easy to use; if you are using them on a machine that is administered by someone else, you can focus on sections (1) and (2) to learn how to use the programs. If you are installing the programs on your own machine, you will need to read section (3) carefully. FASTA and BLAST – FASTA and BLAST have the same goal: to identify statistically signifi- cant sequence similarity that can be used to infer homology. The FASTA programs offer several advantages over BLAST: 1. -
Can Hybridization Be Detected Between African Wolf and Sympatric Canids?
Can hybridization be detected between African wolf and sympatric canids? Sunniva Helene Bahlk Master of Science Thesis 2015 Center for Ecological and Evolutionary Synthesis Department of Bioscience Faculty of Mathematics and Natural Science University of Oslo, Norway © Sunniva Helene Bahlk 2015 Can hybridization be detected between African wolf and sympatric canids? Sunniva Helene Bahlk http://www.duo.uio.no/ Print: Reprosentralen, University of Oslo II Table of contents Acknowledgments ...................................................................................................................... 1 Abstract ...................................................................................................................................... 3 Introduction ................................................................................................................................ 5 The species in the genus Canis are closely related and widely distributed ........................... 5 Next-generation sequencing and bioinformatics .................................................................. 6 The concepts of hybridization and introgression ................................................................... 8 The aim of my study ............................................................................................................... 9 Materials and Methods ............................................................................................................ 11 Origin of the samples and laboratory protocols ................................................................. -
Bioinformatics: a Practical Guide to the Analysis of Genes and Proteins, Second Edition Andreas D
BIOINFORMATICS A Practical Guide to the Analysis of Genes and Proteins SECOND EDITION Andreas D. Baxevanis Genome Technology Branch National Human Genome Research Institute National Institutes of Health Bethesda, Maryland USA B. F. Francis Ouellette Centre for Molecular Medicine and Therapeutics Children’s and Women’s Health Centre of British Columbia University of British Columbia Vancouver, British Columbia Canada A JOHN WILEY & SONS, INC., PUBLICATION New York • Chichester • Weinheim • Brisbane • Singapore • Toronto BIOINFORMATICS SECOND EDITION METHODS OF BIOCHEMICAL ANALYSIS Volume 43 BIOINFORMATICS A Practical Guide to the Analysis of Genes and Proteins SECOND EDITION Andreas D. Baxevanis Genome Technology Branch National Human Genome Research Institute National Institutes of Health Bethesda, Maryland USA B. F. Francis Ouellette Centre for Molecular Medicine and Therapeutics Children’s and Women’s Health Centre of British Columbia University of British Columbia Vancouver, British Columbia Canada A JOHN WILEY & SONS, INC., PUBLICATION New York • Chichester • Weinheim • Brisbane • Singapore • Toronto Designations used by companies to distinguish their products are often claimed as trademarks. In all instances where John Wiley & Sons, Inc., is aware of a claim, the product names appear in initial capital or ALL CAPITAL LETTERS. Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration. Copyright ᭧ 2001 by John Wiley & Sons, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic or mechanical, including uploading, downloading, printing, decompiling, recording or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the Publisher. -
Need and Role of Scala Implementations in Bioinformatics
(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 2, 2017 Need and Role of Scala Implementations in Bioinformatics Abbas Rehman Muhammad Atif Sarwar Department of Computer Science Department of Computer Science COMSATS Institute of Information Technology COMSATS Institute of Information Technology Sahiwal, Pakistan Sahiwal, Pakistan Ali Abbas Javed Ferzund Department of Computer Science Department of Computer Science COMSATS Institute of Information Technology COMSATS Institute of Information Technology Sahiwal, Pakistan Sahiwal, Pakistan Abstract—Next Generation Sequencing has resulted in the evolutionary change in data generation of different sequences. generation of large number of omics data at a faster speed that NGS machines are generating a huge amount of sequence data was not possible before. This data is only useful if it can be stored per day that needs to be stored, analyzed and managed well to and analyzed at the same speed. Big Data platforms and tools like seek the maximum advantages from this. Existing Apache Hadoop and Spark has solved this problem. However, bioinformatics techniques, tools or software are not keeping most of the algorithms used in bioinformatics for Pairwise pace with the speed of data generation. Old Bioinformatics alignment, Multiple Alignment and Motif finding are not tools have very less performance, accuracy and scalability implemented for Hadoop or Spark. Scala is a powerful language while analyzing large amount of data. When storing, managing supported by Spark. It provides, constructs like traits, closures, and analyzing large amount of data which is being generated functions, pattern matching and extractors that make it suitable now a days, these tools require more time and cost with less for Bioinformatics applications.